Hypoxia‐induced sympathetic long‐term facilitation is mediated by a rightward shift in sympathetic action potential amplitude distribution and baroreflex resetting of action potential clusters
Bibliographic record
Abstract
Introduction Baroreflex resetting permits sympathetic long‐term facilitation (sLTF) following hypoxia. Muscle sympathetic nerve activity (MSNA) bursts are generated by synchronous discharge of varying‐amplitude action potentials (APs), with medium APs under strong baroreflex control. AP discharge strategies and baroreflex control of AP clusters facilitating sLTF is unknown. We hypothesized that recruitment of previously latent, large‐amplitude APs and baroreflex resetting of AP cluster operating points (OPs) would mediate sLTF following acute hypoxia. Methods Eight men (age = 24±3 yrs; BMI = 24±3 kg/m 2 ) were exposed to 20 min isocapnic hypoxia (P ET O 2 : 47±2 mmHg) and 30 min recovery. Blood pressure (BP; photoplethysmography) and MSNA (fibular microneurography) were acquired during baseline, hypoxia, early (first 5‐min) and late recovery (last 5‐min). Multi‐unit MSNA burst frequency (BF) and total activity (TA) were quantified. A continuous wavelet transform with matched mother wavelet was used to extract sympathetic APs. AP frequency, AP amplitude (normalized % of largest baseline AP amplitude), percent APs occurring outside a MSNA burst (% asynchronous APs) and total AP clusters was calculated. The proportion of APs firing in small (1‐3), medium (4‐6) and large (7‐10) normalized cluster sizes was assessed. Baroreflex OP was measured by plotting the intersection point of mean cluster incidence and mean diastolic BP (DBP). Friedman repeated‐measures analysis of variance on ranks was used to determine the effect of condition (baseline, hypoxia, early, late). Data are means ± standard deviation or 95% confidence intervals. Results Hypoxia increased BF (P<0.01), TA (P<0.01), AP frequency (Δ124{‐30, 279} AP/min, P<0.05), AP amplitude (Δ3{1, 5} %, P<0.05) and decreased asynchronous APs (Δ‐10{‐17, ‐4} %, P<0.03). Compared to baseline, BF (P<0.03), TA (P<0.02) and AP amplitude (early: Δ3{0, 5} %, P<0.05; late: Δ4{1, 6} %, P<0.05) was elevated during recovery while asynchronous APs (early: Δ‐9{‐16, ‐3} %, P<0.03; late: Δ‐7{‐14, ‐1} %, P<0.03) were reduced. The total number of AP clusters was increased (P<0.05) with no one condition different compared to baseline (hypoxia: Δ3{‐1, 7} clusters; early and late: Δ3{‐1, 6} clusters, P>0.10). Proportion of APs in small clusters was reduced in hypoxia (hypoxia: 44±18 %, P<0.05), early (46±21 %, P<0.05) and late recovery (44±17 %, P<0.05) compared with baseline (53±20 %) while the proportion of APs in large clusters was increased in early recovery (7±6 %, P<0.05) compared with baseline (5±5 %). Baroreflex OPs were shifted rightward for all AP clusters in recovery (baseline DBP: 63±5; early DBP: 64±5; late DBP: 65±4, mmHg; P<0.05) with no effect on slope (P>0.20). Conclusions Hypoxia‐induced sLTF is mediated by reduced asynchronous AP firing, a proportional shift toward large‐amplitude AP activity, and baroreflex resetting of AP clusters to higher OPs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".